For this task, we changed the neighborhood aggregation function \(f_{n}\)  to align better with the task, by using the continuous kernel-based convolutional operator proposed by [1]} for the message pre-processing function \(\phi _{N(v)\rightarrow v}\) . This convolution is used by all of the baseline models we consider. Similarly, to stay close to baseline models we parameterize both the final readout \(f_o\)  and the \(\phi _{a\rightarrow v}\)  function used for agent aggregation in the node update by 2-layer MLPs. We also use the global information exchange between the agents, both in the node update step and in the agent update step. Otherwise, the training procedure is the same as for the other tasks.
